Precision Oncology, informatics and computation keynote at Northwestern University OncoSET Symposium, April 28, 2017

1. Precision Medicine in the Age
of NCI MATCH and the Beau
Biden Cancer Moonshot
Warren Kibbe, Ph.D.
Acting Deputy Director
National Cancer Center
Rockville, MD

2. Precision Medicine in the Age of NCI
MATCH and the Beau Biden Cancer
Moonshot
Warren Kibbe, PhD
warren.kibbe@nih.gov
@wakibbe
April 28th, 2017

3. 3
Outline
1. Motivation
2. Data & Computation in
Biomedicine
3. NCI MATCH
4. Cancer Moonshot
5. Data Commons
Thanks to many folks for slides, but especially Dr. Jerry Lee

4. 4
In 2016 there were an estimated
1,700,000 new cancer cases
and
600,000 cancer deaths
- American Cancer Society
Cancer remains the second most common cause of
death in the U.S.
- Centers for Disease Control and Prevention

5. 5
In 2016 there were an estimated
15,500,000
cancer survivors in the U. S.

6. 6
Understanding Cancer
 Precision medicine will lead to fundamental
understanding of the complex interplay between
genetics, epigenetics, nutrition, environment and clinical
presentation and direct effective, evidence-based
prevention and treatment.

7. 7
(10,000+ patient tumors and increasing)
Courtesy of P. Kuhn (USC)
2006-2015:
A Decade of Illuminating the
Underlying Causes of Primary
Untreated Tumors Omics
Characterization
Cancer is a grand challenge
Deep biological understanding
Advances in scientific methods
Advances in instrumentation
Advances in technology
Data and computation
Mathematical models
Cancer Research and Care generate
detailed data that is critical to
create a learning health system for cancer
Requires:

8. 8
(10,000+ patient tumors and increasing)
Courtesy of P. Kuhn (USC)
2006-2015:
A Decade of Illuminating the
Underlying Causes of Primary
Untreated Tumors Omics
Characterization

9. 9

10. Biological Scales

11. 11

12. 12
Keeping in mind cellular dynamics
On average across 375
tumor samples, ONLY
33% of RNA expression
differences correlated
with protein abundance
Zhang B et al, Proteogenomic characterization of human colon and rectal cancer, Nature, 2014, July 20.

13. 13
" there is great potential for new insights to come
from the combined analysis of cancer proteomic
and genomic data, as proteomic data can now
reproducibly provide information about protein
levels and activities that are difficult or impossible
to infer from genomic data alone ”
Douglas R. Lowy, MD
Acting Director of the National Cancer Institute, National Institutes of Health
5/25/2016

14. 14
Genomics is the beginning of
precision medicine, not the end!

15. 15
1997 20152001 20051998 20142002 20061999 2003 20112000 2004 2007 20122008 20132009 2010
10/23/2001
(~5 yrs old)
4/21/1997
1/9/2007
(~10 yrs old)
iPod (10GB max)
WinAMP(mp3)
iPhone (EDGE, 16 GB max)
9/16/1999
(~3 yrs old)
802.11b WiFi
4/3/2010
(~13 yrs old)
iPad (EDGE, 64 GB max)
4/23/2005
(~8 yrs old)
9/26/2006
(~9 yrs old)
7/15/2006
2/7/2007
Google
Drive
4/24/2012
(~15 yrs old)7/11/2008
(~11 yrs old)
iPhone 3G
(16 GB max)
9/12/2012
(~15 yrs old)
iPhone5 (LTE, 128 GB max)
Google
Baseline
7/14/2014
(~17 yrs old)
3/9/2015
(~18 yrs old)
Digital technology is changing rapidly

16. 16
$640M
(FY74)
$5.21 B
(FY16)
Cancer therapy is changing

17. 17
18
Application of Cancer Genomics is changing
https://www.cancer.gov/about-cancer/treatment/clinical-trials/nci-supported/nci-match

18. NCI MATCH
•Conduct across 2400 NCI-supported sites
•Pay for on-study and at progression biopsies
•Screen 5000 patients to complete
30 phase II trials; target 25% ‘rare’ tumors;
1CR, PR, SD, and PD as defined by RECIST
2Stable disease is assessed relative to tumor status at re-initiation of study agent
3Rebiopsy; if additional mutations, offer new targeted therapy
,2
https://www.cancer.gov/about-cancer/treatment/clinical-trials/nci-supported/nci-match

19. 19
MATCH Assay: Workflow for 12-14 Day Turnaround
Tissue Fixation
Path Review
Nucleic Acid
Extraction
Library/Template Prep
Sequencing , QC
Checks
Clinical
Laboratory
aMOI
Verification
Biopsy Received at Quality Control Center
1 DAY
1 DAY
1 DAY
1 DAY
3 DAYS
10-12 days
Tumor content >70%
Centralized Data
Analysis
DNA/RNA yields >20 ng
Library yield >20 pM
Test fragments
Total read
Reads per BC
Coverage
NTC, Positive, Negative
Controls
aMOIs Identified
Rules Engine
Treatment
Selection
3-5 DAYS

20. 20
MATCH Observations
 MATCH is open nationwide at ~1500 NCORP and NCTN sites
 Accrual has been 100-150 patients / week
 Match rate was initially ~8% for first 8 arms
 After reopening May 30, 2016 rate has been ~20% for 20+ arms
 Processing has been holding to 12-14 days average
 Interest has been high
https://www.cancer.gov/about-cancer/treatment/clinical-trials/nci-supported/nci-match

21. 21
Precision Oncology
 It isn’t just about matching patients to therapy, it is also about avoiding
therapies that will not work.
 Biology is complex, and we still have a lot of basic biology to
understand
 Genomics+imaging+clinical labs is the first wave of precision oncology

22. 22
Nature of Science is changing
Scale
Precision
Complexity
Opportunity

23. 23

24. 24
June 2016

25. 25
Biology and Medicine are now data
intensive enterprises
Scale is rapidly changing
Technology, data, computing and IT are
pervasive in the lab, the clinic, in the
home, and across the population

26. 26

27. 27
Sebastian Thrun

28. 28
4/3/2017

29. 29
Expert Systems vs Machine Learning
 In 1945, the British philosopher Gilbert Ryle
identified two kinds of knowledge— factual,
propositional knowledge that can be ordered into
rules—“knowing that.” versus implicit,
experiential, skill-based—“knowing how.”
 Machine Learning is based on ‘learning how’.
Expert systems, or rule based machines, are
based on ‘knowing that’.

30. 30
Human Cognition
Three kinds of learning:
 Learning that – rule-based knowledge
 Learning how – experiential knowledge
 Learning why – integrative, explanatory knowledge

31. 31

32. 32

33. 33

34. 34
The Beau Biden Cancer Moonshot
• Accelerate progress in cancer,
including prevention & screening
• From cutting edge basic research to
wider uptake of standard of care
• Encourage greater cooperation
and collaboration
• Within and between academia,
government, and private sector
• Enhance data sharing
Blue Ribbon Panel recommendations (Oct ‘16); Implementation Working Groups established (Jan ‘17)
cancer.gov/brp

35. 35
Relationship Between Bypass Budget and Blue Ribbon Panel Report
• Bypass Budget addresses NCI’s
entire research portfolio
• Lays out the plan for NCI’s
continued investment in cancer
research
• Cancer Moonshot is a unique
opportunity to enhance cancer
research in specific areas that are
poised for acceleration
• The BRP report made 10 bold, yet
feasible, recommendations that
will fast-track initiatives if infused
with Moonshot funding

36. 36
A Few Beau Biden Cancer Moonshot Milestones
• Announced by Former President Obama at the State of the Union January 12, 2016
• Blue Ribbon Panel convened at AACR, April 18, 2016
• Genomic Data Commons went public June 6, 2016
• Vice President’s Cancer Moonshot Summit – June 29, 2016
• Rethinking Clinical Trial Search – Open API at https://clinicaltrialsapi.cancer.gov
• Blue Ribbon Panel recommendations – accepted by the National Cancer Advisory Board on
September 7th, 2016
• Cancer Moonshot Task Force and BRP recommendations sent to President on October 17th,
2016 https://www.cancer.gov/research/key-initiatives/moonshot-cancer-initiative/milestones
and released at https://cancer.gov/brp
• 21st Century Cures Act funding the Beau Biden Cancer Moonshot passed 94-5 by the Senate
on December 8 and signed by Former President Obama December 13, 2016.

37. 37
• 28 Members
• Clinicians, researchers, advocates, pharma and tech industries
• Three face-to-face meetings to identify “Moonshot”
recommendations
• 7 Working Groups
• Clinical trials, enhanced data sharing, cancer immunology, tumor
evolution, implementation science, pediatric cancer, precision
prevention and early detection
• Met weekly for 6 weeks to generate 2-3
recommendations/working group
• More than 150 people were part of the working group
Blue Ribbon Panel: Members & Working Groups

38. 38
https://cancer.gov/brp

39. Blue Ribbon Panel Recommendations
• Network for Direct Patient Engagement
• Cancer Immunotherapy Translational Science Network
• Therapeutic Target Identification to Overcome Drug Resistance
• A National Cancer Data Ecosystem for Sharing and Analysis
• Fusion Oncoproteins in Childhood Cancers
• Symptom Management Research
• Prevention and Early Detection – Implementation of Evidence-based Approaches
• Retrospective Analysis of Biospecimens from Patients Treated with Standard of
Care
• Generation of 4D Human Tumor Atlas
• Development of New Enabling Cancer Technologies

40. 40
Beau Biden Cancer Moonshot Funding Opportunities
40
https://www.cancer.gov/research/key-initiatives/moonshot-cancer-initiative/funding
14 RFAs as of April 21, 2017

41. 41
NCI Data Commons
Data sharing,
Architecture,
Genomics

42. 42
Cancer Data Sharing
& Data Commons
• Support open science
• Support data reusability
• Aligned with Cancer Moonshot
• Part of Precision Medicine
• Reduce Health Disparities
• Improve patient access to clinical
trials
• Toward a learning National Cancer
Data Ecosystem
Reduce the risk, improve early detection, outcomes and survivorship in cancer

43. 43
Changing the conversation around data sharing
 How do we find data, software, standards?
 How can we make data, annotations, software, metadata accessible?
 How do we reuse data standards?
 How do we make more data machine readable?
NIH Data Commons
NCI Genomic Data Commons
National Cancer Data Ecosystem
Data Commons co-locate data, storage and computing infrastructure, and
frequently used tools for analyzing and sharing data to create an
interoperable resource for the research community.
*Robert L. Grossman, Allison Heath, Mark Murphy, Maria Patterson, A Case for Data Commons Towards Data Science as a
Service, to appear. Source of image: Interior of one of Google’s Data Center, www.google.com/about/datacenters/.

44. Cancer Research Data Ecosystem – Cancer Moonshot BRP
Well characterized
research data sets Cancer cohorts Patient data
EHR, Lab Data, Imaging,
PROs, Smart Devices,
Decision Support
Learning from every
cancer patient
Active research
participation
Research information
donor
Clinical Research
Observational studies
Proteogenomics
Imaging data
Clinical trials
Discovery
Patient engaged
Research
Surveillance
Big Data
Implementation research
SEERGDC

45. Data Commons Framework

46. 46
GDC as an example of a new
architecture for storing and sharing
cancer data

47. 47
The Cancer Genomic Data Commons
(GDC) is an existing effort to standardize
and simplify submission of genomic data
to NCI and follow the principles of FAIR
– Findable, Accessible, Attributable,
Interoperable, Reusable, and Provide
Recognition.
The GDC is part of the NIH Big Data to
Knowledge (BD2K) initiative and an
example of the NIH Commons
Genomic Data Commons
Microattribution, nanopublications, tracking the use of
data, annotation of data, use of algorithms, supports
the data /software /metadata life cycle to provide
credit and analyze impact of data, software, analytics,
algorithm, curation and knowledge sharing
Force11 white paper
https://www.force11.org/group/fairgroup/fairprinciples

48. NCI Genomic Data Commons
 The GDC went live on June 6th, 2016 with approximately 4.1 PB of data.
 577,878 files about 14194 cases (patients), in 42 cancer types, across 29 primary
disease sites, 400 clinical data elements
 10 major data types, ranging from Raw Sequencing Data, Raw Microarray Data, to
Copy Number Variation, Simple Nucleotide Variation and Gene Expression.
 Data are derived from 17 different experimental strategies, with the major ones
being RNA-Seq, WXS, WGS, miRNA-Seq, Genotyping Array and Expression Array.
 Foundation Medicine announced the release of 18,000 genomic profiles to the GDC
at the Cancer Moonshot Summit, June 29th, 2016
 The Multiple Myeloma Research Foundation announced it would be releasing its
CoMMpass study of more than 1000 cases of Multiple Myeloma on Sept 29th, 2016.

49. 49
NCI Cancer Genomics Cloud Pilots
Democratize access to
NCI-generated genomic
and related data, and to
create a cost-effective
way to provide scalable
computational capacity
to the cancer research
community.
Cloud Pilots provide:
• Access to large genomic data sets without need to download
• Access to popular pipelines and visualization tools
• Ability for researchers to bring their own tools and pipelines to the data
• Ability for researchers to bring their own data and analyze in combination with existing genomic
data
• Workspaces, for researchers to save and share their data and results of analyses

50. 50
• PI: Gad Getz
• Google Cloud
• Firehose in the cloud including Broad best practices workflows
•http://firecloud.org
Broad Institute
• PI: Ilya Shmulevich
• Google Cloud
• Leverage Google infrastructure; Novel query and visualization
•http://cgc.systemsbiology.net/
Institute for
Systems Biology
• PI: Deniz Kural
• Amazon Web Services
• Interactive data exploration; > 30 public pipelines
•http://www.cancergenomicscloud.org
Seven Bridges
Genomics
Three NCI Genomics Cloud Pilots
Selection
Design/Build
I
Design/Build
II
Evaluation Extension
Sept 2016Jan 2016April 2015Sept 2014
Jan 2014

51. SBG CGC
Broad FireCloud ISB CGC
Researchers
APIs
Web Interface
APIs
Web Interface
Data Submission
& Harmonization
Genomics Cloud Pilots:
Visualization, Compute,
Pipelines, WorkspacesAuthentication
& Authorization thru
eRA Commons & dbGaP
GDC
GDC / Cloud Pilots Framework: Today
Genomic Data
Commons:
Harmonization,
Visualization,
& Download

52. Researchers
Web Interface Web Interface
Data Submission
& Harmonization
Authentication
& Authorization thru
eRA Commons & dbGaP
GDC
GDC / Cloud Pilots Framework: Near Future
Broad FireCloud
ISB CGC
SBG CGC
GDC@AWS
GDC@GCP
GDC@Azure DockStore
Analysis resources
APIs APIs

53. The NCI Cancer Research Data Commons Vision:
A virtual, expandable infrastructure
GDC
Clinical
Functional
Cancer Models
Imaging
Population
Proteomics
NCI Cancer Research
Data Commons
GDC
Researcher
s
Patients
Clinician
s
Authentication
& Authorization
Multiple Cloud-based Commons Nodes
• Interoperable through data standards and common identifiers
• Data are validated & harmonized using pre-defined processes as agreed by the community
• Secure access to controlled data to protect patient privacy

54. Node A
Cloud X
NCI Cancer Research Data Commons:
An Individual Node
Cloud Y
Data
Contributors
Data
Submission
Data
Mirroring

55. Development of the NCI Genomic Data Commons (GDC)
To Foster the Molecular Diagnosis and Treatment of Cancer
GDC
Bob Grossman PI
Univ. of Chicago
Ontario Inst. Cancer Res.
Leidos
Institute of Medicine
Towards Precision Medicine
2011

58. Discovery of Cancer Drivers With 2% Prevalence
Lung adeno.
+ 2,900
Colorectal
+ 1,200
Ovarian
+ 500
Lawrence et al, Nature 2014
Power Calculation for Cancer Driver Discovery
Need to resequence >100,000 tumors to
identify all cancer drivers at >2% prevalence

59. Workspace –
isolated environment for collaborative analysis
Data + Methods → Results
sample data and
metadata (e.g.
BAMs, tissue type)
algorithms
(e.g. mutation
calling)
Wiring logic
(e.g. use the exome
capture BAM)
executions and results
(e.g. run mutation caller v41
on this exact bam and track
results)
Slide courtesy of Broad Institute

60. GDC Acknowledgements
NCI Center for Cancer Genomics Univ. of Chicago
Bob Grossman
Allison Heath
Mike Ford
Zhenyu Zhang
Ontario Institute for Cancer Research
Lou Staudt
Zhining Wang
Martin Ferguson
JC Zenklusen
Daniela Gerhard
Deb Steverson
Vincent Ferretti
'Francois Gerthoffert
JunJun Zhang
Leidos Biomedical Research
Mark Jensen
Sharon Gaheen
Himanso Sahni
NCI NCI CBIIT
Tony Kerlavage
Tanya Davidsen

61. CGC Pilot Team Principal Investigators
• Gad Getz, Ph.D - Broad Institute - http://firecloud.org
• Ilya Shmulevich, Ph.D - ISB - http://cgc.systemsbiology.net/
• Deniz Kural, Ph.D - Seven Bridges – http://www.cancergenomicscloud.org
NCI Project Officer & CORs
• Anthony Kerlavage, Ph.D –Project Officer
• Juli Klemm, Ph.D – COR, Broad Institute
• Tanja Davidsen, Ph.D – COR, Institute for Systems Biology
• Ishwar Chandramouliswaran, MS, MBA – COR, Seven Bridges Genomics
GDC Principal Investigator
• Robert Grossman, Ph.D - University of Chicago
• Allison Heath, Ph.D - University of Chicago
• Vincent Ferretti, Ph.D - Ontario Institute for Cancer Research
Cancer Genomics Project Teams
NCI Leadership Team
• Doug Lowy, M.D.
• Lou Staudt, M.D., Ph.D.
• Stephen Chanock, M.D.
• George Komatsoulis, Ph.D.
• Warren Kibbe, Ph.D.
Center for Cancer Genomics Partners
• JC Zenklusen, Ph.D.
• Daniela Gerhard, Ph.D.
• Zhining Wang, Ph.D.
• Liming Yang, Ph.D.
• Martin Ferguson, Ph.D.

64. 64
Integrated data sets, interoperable
resources, harmonized data are
necessary for and enable
biologically informed cancer
computational predictive models

65. 65
Questions?
Warren Kibbe, Ph.D.
Warren.kibbe@nih.gov
@wakibbe

66. www.cancer.gov www.cancer.gov/espanol

67. 67
NIH Genomic Data Sharing Policy
https://gds.nih.gov/
Went into effect January 25, 2015
NCI guidance:
http://www.cancer.gov/grants-training/grants-
management/nci-policies/genomic-data
Requires public sharing of genomic data sets